Principles of Database Management

The Practical Guide to Storing, Managing and Analyzing Big and Small Data

To be published by Cambridge University Press

This comprehensive textbook teaches the fundamentals of database design, modeling, systems, data storage, and the evolving world of data warehousing, governance and more. Written by experienced educators and experts in big data, analytics, data quality, and data integration, it provides an up-to-date approach to database management. This full-color, illustrated text has a balanced theory-practice focus, covering essential topics, from established database technologies to recent trends, like Big Data, NoSQL, and more. Fundamental concepts are supported by real-world examples, query and code walkthroughs, and figures, making it perfect for introductory courses for advanced undergraduates and graduate students in information systems or computer science. These examples are further supported by an online playground with multiple learning environments, including MySQL; MongoDB; Neo4j Cypher; and tree structure visualization. This combined learning approach connects key concepts throughout the text to the important, practical tools to get started in database administration.

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Features

Principles of Database Management provides students with the comprehensive database management information to understand and apply the fundamental concepts of database design and modeling, database systems, data storage, and the evolving world of data warehousing, governance and more. Designed for those studying database management for information management or computer science, this well-illustrated textbook has a well-balanced theory-practice focus and covers the essential topics, from established database technologies up to recent trends like Big Data, NoSQL, and analytics. On-going case studies, drill-down boxes that reveal deeper insights on key topics, retention questions at the end of every section of a chapter, and connections boxes that show the relationship between concepts throughout the text are included to provide the practical tools to get started in database administration.

Given the above considerations, the key distinctive features of our book are:

Data Services and Data Flows in the Context of Data and Process Integration

Searching Unstructured Data and Enterprise Search

Principles of Full Text Search

Indexing Full Text Documents

Web Search Engines

Enterprise Search

Data Quality and Master Data Management

Data Governance

Total Data Quality Management (TQDM)

Capability Maturity Model Integration (CMMI)

Data Management Body of Knowledge (DMBOK)

Control Objectives for Information and Related Technology (COBIT)

Information Technology Infrastructure Library (ITIL)

Outlook

Chapter 19: Big Data (Show/hide details)

The 5 V's of Big Data

Hadoop

History of Hadoop

The Hadoop Stack

SQL on Hadoop

HBase: The First Database on Hadoop

Pig

Hive

Apache Spark

Spark Core

Spark SQL

MLlib, Spark Streaming and GraphX

Chapter 20: Analytics (Show/hide details)

The Analytics Process Model

Example Analytics Applications

Data Scientist Job Profile

Data Preprocessing

Denormalizing Data for Analysis

Sampling

Exploratory Analysis

Missing Values

Outlier Detection and Handling

Types of Analytics

Predictive Analytics

Evaluating Predictive Models

Descriptive Analytics

Social Network Analytics

Post Processing of Analytical Models

Critical Success Factors for Analytical Models

Economic Perspective on Analytics

Total Cost of Ownership (TCO)

Return on Investment (ROI)

In- versus Outsourcing

On-Premise versus Cloud Solutions

Open Source versus Commercial Software

Improving the ROI of Analytics

New Sources of Data

Data Quality

Management Support

Organizational Aspects

Cross-Fertilization

Privacy and Security

Overall Considerations Regarding Privacy and Security

The RACI Matrix

Accessing Internal Data

Privacy Regulation

What you will find on this site

After the book is released, readers will be able to work with an interactive environment to:

Play around with SQL queries

Play around with a MongoDB NoSQL database

Play around with a Neo4j graph database

Play around with an HBase database

In addition, we will provide extra material (video lectures, slides, ...) and maintain an errata list here as well.

About the authors

Wilfried Lemahieu is a professor at KU Leuven, Faculty of Economics and Business, where he also holds the position of Dean. His teaching, for which he was awarded a ‘best teacher recognition’ includes Database Management, Enterprise Information Management and Management Informatics. His research focuses on big data storage and integration, data quality, business process management and service-oriented architectures. In this context, he collaborates extensively with a variety of industry partners, both local and international. His research is published in renowned international journals and he is a frequent lecturer for both academic and industry audiences.
See feb.kuleuven.be/wilfried.lemahieu
for further details.

Bart Baesens is a professor of Big Data and Analytics at KU Leuven (Belgium) and a lecturer at the University of Southampton (United Kingdom). He has done extensive research on Big Data & Analytics, Credit Risk Modeling, Fraud Detection and Marketing Analytics. He wrote more than 200 scientific papers some of which have been published in well-known international journals (e.g. MIS Quarterly, Machine Learning, Management Science, MIT Sloan Management Review and IEEE Transactions on Knowledge and Data Engineering) and presented at international top conferences (e.g. ICIS, KDD, CAISE). He received various best paper and best speaker awards. Bart is the author of 6 books: Credit Risk Management: Basic Concepts (Oxford University Press, 2009), Analytics in a Big Data World (Wiley, 2014), Beginning Java Programming (Wiley, 2015), Fraud Analytics using Descriptive, Predictive and Social Network Techniques (Wiley, 2015), Credit Risk Analytics (Wiley, 2016) and Profit-Driven Business Analytics (Wiley, 2017). He sold more than 15.000 copies of these books worldwide, some of which have been translated in Chinese, Russian and Korean. His research is summarized at
www.dataminingapps.com.
He also regularly tutors, advises and provides consulting support to international
firms with respect to their big data, analytics and credit risk management strategy.

Seppe vanden Broucke works as an assistant professor at the Faculty of Economics and Business, KU Leuven, Belgium. His research interests include business data mining and analytics, machine learning, process management and process mining. His work has been published in well-known international journals and presented at top conferences. He is also author of the book Beginning Java Programming (Wiley, 2015) of which more than 4000 copies were sold and which was also translated in Russian. Seppe's teaching includes Advanced Analytics, Big Data and Information Management courses. He also frequently teaches for industry and business audiences.
See seppe.net for further details.